Gradient-based attribution is one model-specific sub-field within post-hoc explainability that seeks to generate attributions for neural network inputs. Common to other XAI methods, it is difficult to determine if, and under which conditions, these gradient-based explanations are valid. [1]} demonstrate through randomization tests that there are certain conditions where popular gradient-based methods appear independent to training data permutations and model parameter changes. They show that the combination of these inadequate methods with misleading visualizations underscore the need for guidelines and sanity checks [1]}. Guidance is not only needed for gradient-based attribution methods, but also for popular XAI methods such as SHAP.
